 # Chapter 7. Simulation Methods for VaR for Options and Bonds (in R/MATLAB)

Copyright 2011 - 2019 Jon Danielsson. This code is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This code is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. The GNU General Public License is available at: https://www.gnu.org/licenses/.

##### Listing 7.1/7.2: Transformation in R Last updated 2011

x=seq(-3,3,by=0.1)
plot(x,pnorm(x),type="l")

##### Listing 7.1/7.2: Transformation in MATLAB Last updated 2011

x=-3:0.1:3;
plot(x,normcdf(x))


##### Listing 7.3/7.4: Various RNs in R Last updated 2011

set.seed(12) # set seed
S=10
runif(S)
rnorm(S)
rt(S,4)

##### Listing 7.3/7.4: Various RNs in MATLAB Last updated August 2016

rng default; % set seed
S=10;
rand(S,1)
randn(S,1)
trnd(4,S,1)


##### Listing 7.5/7.6: Price bond in R Last updated August 2019

yield=c(5.00, 5.69, 6.09, 6.38, 6.61,
6.79, 6.94, 7.07, 7.19, 7.30)       # yield curve
r=0.07                                      # initial yield rate
Par=10                                      # par value
coupon=r*Par                                # coupon payments
cc=1:10*0+coupon                            # vector of cash flows
cc=cc+Par                           # add par to cash flows
P=sum(cc/((1+yield/100)^(1:length(yield)))) # calculate price
print(P)

##### Listing 7.5/7.6: Price bond in MATLAB Last updated 2011

yield = [5.00 5.69 6.09 6.38 6.61...
6.79 6.94 7.07 7.19 7.30];  % yield curve
T = length(yield);
r=0.07;                              % initial yield rate
Par=10;                              % par value
coupon=r*Par;                        % coupon payments
cc=zeros(1,T)+coupon;                % vector of cash flows
cc(10)=cc(10)+Par;                   % add par to cash flows
P=sum(cc./((1+yield./100).^(1:T)))   % calculate price


##### Listing 7.7/7.8: Simulate yields in R Last updated August 2019

set.seed(12)                             # set seed
sigma = 1.5                              # daily yield volatiltiy
S = 8                                    # number of simulations
r = rnorm(S,0,sigma)                     # generate random numbers
ysim = matrix(nrow=length(yield),ncol=S)
for (i in 1:S) ysim[,i]=yield+r[i]
matplot(ysim,type='l')

##### Listing 7.7/7.8: Simulate yields in MATLAB Last updated 2011

randn('state',123);                    % set the seed
sigma = 1.5;                           % daily yield volatility
S = 8;                                 % number of simulations
r = randn(1,S)*sigma;                  % generate random numbers
ysim=nan(T,S);
for i=1:S
ysim(:,i)=yield+r(i);
end
ysim=repmat(yield',1,S)+repmat(r,T,1);
plot(ysim)


##### Listing 7.9/7.10: Simulate bond prices in R Last updated August 2019

SP = vector(length=S)
for (i in 1:S){                                      # S simulations
SP[i] = sum(cc/((1+ysim[,i]/100)^(length(yield))))
}
SP = SP-(mean(SP) - P)                               # correct for mean
par(mfrow=c(1,2), pty="s")
barplot(SP)
hist(SP,probability=TRUE)
x=seq(6,16,length=100)
lines(x, dnorm(x, mean = mean(SP), sd = sd(SP)))

##### Listing 7.9/7.10: Simulate bond prices in MATLAB Last updated 2011

SP = nan(S,1);
for s = 1:S                                        % S simulations
SP(s) = sum(cc./(1+ysim(:,s)'./100).^((1:T)));
end
SP = SP-(mean(SP) - P);                            % correct for mean
bar(SP)


##### Listing 7.11/7.12: Black-Scholes valuation in R Last updated August 2019

P0 = 50                            # initial spot price
sigma = 0.2                        # annual volatility
r = 0.05                           # annual interest
TT = 0.5                           # time to expiration
X = 40                             # strike price
f = bs(X,P0,r,sigma,length(yield)) # analytical call price
## this calculation uses the Black-Scholes pricing function (Listing 6.1/6.2)
print(f)

##### Listing 7.11/7.12: Black-Scholes valuation in MATLAB Last updated 2011

P0 = 50;               % initial spot price
sigma = 0.2;           % annual volatility
r = 0.05;              % annual interest
T = 0.5;               % time to expiration
X = 40;                % strike price
f = bs(X,P0,r,sigma,T) % analytical call price
%% this calculation uses the Black-Scholes pricing function (Listing 6.1/6.2)


##### Listing 7.13/7.14: Black-Scholes simulation in R Last updated August 2016

set.seed(12)                                                         # set seed
S = 1e6                                                              # number of simulations
F = P0*exp(r*length(yield))                                          # futures price
ysim = rnorm(S,-0.5*sigma^2*length(yield),sigma*sqrt(length(yield))) # sim returns, lognorm corrected
F=F*exp(ysim)                                                        # sim futures price
SP = F-X                                                             # payoff
SP[SP<0] = 0                                                         # set negative outcomes to zero
fsim = SP*exp(-r*length(yield))                                      # discount
call_sim = mean(fsim)                                                # simulated price
print(call_sim)

##### Listing 7.13/7.14: Black-Scholes simulation in MATLAB Last updated 2011

randn('state',0);                            % set seed
S = 1e6;                                     % number of simulations
F = P0*exp(r*T);                             % futures price
ysim=randn(S,1)*sigma*sqrt(T)-0.5*T*sigma^2; % sim returns, lognorm corrected
F=F*exp(ysim);                               % sim futures price
SP = F-X;                                    % payoff
SP(find(SP < 0)) = 0;                        % set negative outcomes to zero
fsim =SP*exp(-r*T) ;                         % discount
mean(fsim)                                   % simulated price


##### Listing 7.15/7.16: Option density plots in R Last updated 2011

par(mfrow=c(1,2), pty="s")
hist(F,probability=TRUE,ylim=c(0,0.06))
x=seq(min(F),max(F),length=100)
lines(x, dnorm(x, mean = mean(F), sd = sd(SP)))
hist(fsim,nclass=100,probability=TRUE)

##### Listing 7.15/7.16: Option density plots in MATLAB Last updated 2011

subplot(1,2,1)
histfit(F);
subplot(1,2,2)
hist(fsim,100);


##### Listing 7.17/7.18: Simulate VaR in R Last updated 2011

set.seed(1)                           # set seed
S = 1e7                               # number of simulations
s2 = 0.01^2                           # daily variance
p = 0.01                              # probability
r = 0.05                              # annual riskfree rate
P = 100                               # price today
ysim = rnorm(S,r/365-0.5*s2,sqrt(s2)) # sim returns
Psim = P*exp(ysim)                    # sim future prices
q = sort(Psim-P)                      # simulated P/L
VaR1 = -q[p*S]
print(VaR1)

##### Listing 7.17/7.18: Simulate VaR in MATLAB Last updated 2011

randn('state',0);                        % set seed
S = 1e7;                                 % number of simulations
s2 = 0.01^2;                             % daily variance
p = 0.01;                                % probability
r = 0.05;                                % annual riskfree rate
P = 100;                                 % price today
ysim = randn(S,1)*sqrt(s2)+r/365-0.5*s2; % sim returns
Psim = P*exp(ysim);                      % sim future prices
q = sort(Psim-P);                        % simulated P/L
VaR1 = -q(S*p)


##### Listing 7.19/7.20: Simulate option VaR in R Last updated August 2016

TT = 0.25;                                      # time to expiration
X = 100;                                        # strike price
sigma = sqrt(s2*250);                           # annual volatility
f = bs(X,P,r,sigma,length(yield))               # analytical call price
fsim = bs(X,Psim,r,sigma,length(yield)-(1/365)) # sim option prices
q = sort(fsim$Call-f$Call)                      # simulated P/L
VaR2 = -q[p*S]
print(VaR2)

##### Listing 7.19/7.20: Simulate option VaR in MATLAB Last updated 2011

T = 0.25;                          % time to expiration
X = 100;                           % strike price
sigma = sqrt(s2*250);              % annual volatility
f = bs(X,P,r,sigma,T);             % analytical call price
fsim=bs(X,Psim,r,sigma,T-(1/365)); % sim option prices
q = sort(fsim.Call-f.Call);        % simulated P/L
VaR2 = -q(p*S)


##### Listing 7.21/7.22: Example 7.3 in R Last updated August 2016

X1 = 100
X2 = 110
f1 = bs(X1,P,r,sigma,TT)
f2 = bs(X2,P,r,sigma,TT)
f2sim = bs(X2,Psim,r,sigma,TT-(1/365))
f1sim = bs(X1,Psim,r,sigma,TT-(1/365))
q = sort(f1sim$Call+f2sim$Put+Psim-f1$Call-f2$Put-P);
VaR3 = -q[p*S]
print(VaR3)

##### Listing 7.21/7.22: Example 7.3 in MATLAB Last updated 2011

X1 = 100;
X2 = 110;
f1 = bs(X1,P,r,sigma,T);
f2 = bs(X2,P,r,sigma,T);
f1sim=bs(X1,Psim,r,sigma,T-(1/365));
f2sim=bs(X2,Psim,r,sigma,T-(1/365));
q = sort(f1sim.Call+f2sim.Put+Psim-f1.Call-f2.Put-P);
VaR3 = -q(p*S)


##### Listing 7.23/7.24: Simulated two-asset returns in R Last updated 2011

library (MASS)
set.seed(12)                                      # set seed
mu = c(r/365,r/365)                               # return mean
Sigma = matrix(c(0.01,0.0005,0.0005,0.02),ncol=2) # covariance matrix
y = mvrnorm(S,mu,Sigma)                           # simulated returns

##### Listing 7.23/7.24: Simulated two-asset returns in MATLAB Last updated 2011

randn('state',12)                 % set seed
mu = [r/365 r/365]';              % return mean
Sigma=[0.01 0.0005; 0.0005 0.02]; % covariance matrix
y = mvnrnd(mu,Sigma,S);           % simulated returns


##### Listing 7.25/7.26: Two-asset VaR in R Last updated 2011

K=2
P = c(100,50)                                 # prices
x = c(1,1)                                    # number of assets
Port = P %*% x                                # portfolio at t
Psim = matrix(t(matrix(P,K,S)),ncol=K)*exp(y) # simulated prices
PortSim = Psim %*% x                          # simulated portfolio value
q = sort(PortSim-Port[1,1])                   # simulated P/L
VaR4 = -q[S*p]
print(VaR4)

##### Listing 7.25/7.26: Two-asset VaR in MATLAB Last updated 2011

K = 2;
P = [100 50]';                  % prices
x = [1 1]';                     % number of assets
Port = P'*x;                    % portfolio at t
Psim = repmat(P,1,S)' .*exp(y); % simulated prices
PortSim=Psim * x;               % simulated portfolio value
q = sort(PortSim-Port);         % simulated P/L
VaR4 = -q(S*p)


##### Listing 7.27/7.28: A two-asset case in R with an option Last updated August 2016

f = bs(P,P,r,sigma,TT)
fsim = bs(P,Psim[,2],r,sigma,TT-(1/365))
q = sort(fsim$Call+Psim[,1]-f$Call-P);
VaR5 = -q[p*S]
print(VaR5)

##### Listing 7.27/7.28: A two-asset case in MATLAB with an option Last updated 2011

f = bs(P(2),P(2),r,sigma,T);
fsim=bs(P(2),Psim(:,2),r,sigma,T-(1/365));
q = sort(fsim.Call+Psim(:,1)-f.Call-P(1));
VaR5 = -q(p*S)